def warpANTSAffine(targetName, referenceName, affineName, oname, interpolationType='trilinear'):
baseName=rcommon.getBaseFileName(targetName)
nib_target=nib.load(targetName)
nib_reference=nib.load(referenceName)
M=nib_target.get_affine()
F=nib_reference.get_affine()
referenceShape=np.array(nib_reference.shape, dtype=np.int32)
######Load and compose affine#####
if not affineName:
T=np.eye(4)
else:
T=rcommon.readAntsAffine(affineName)
affineComposition=np.linalg.inv(M).dot(T.dot(F))
######################
if interpolationType=='NN':
target=nib_target.get_data().squeeze().astype(np.int32)
target=np.copy(target, order='C')
warped=np.array(tf.warp_discrete_volumeNNAffine(target, referenceShape, affineComposition)).astype(np.int16)
else:
target=nib_target.get_data().squeeze().astype(np.float64)
target=np.copy(target, order='C')
warped=np.array(tf.warp_volume_affine(target, referenceShape, affineComposition)).astype(np.int16)
warped=nib.Nifti1Image(warped, F)
if not oname:
oname="warped"+baseName+"nii.gz"
warped.to_filename(oname)
def register_3d(params):
r'''
Runs the non-linear registration with the parsed parameters
'''
print('Registering %s to %s'%(params.target, params.reference))
sys.stdout.flush()
####Initialize parameter dictionaries####
metric_name=params.metric[0:params.metric.find('[')]
metric_params_list=params.metric[params.metric.find('[')+1:params.metric.find(']')].split(',')
if metric_name=='EM':
metric_parameters = {
'max_step_length':float(metric_params_list[0]),
'lambda':float(metric_params_list[1]),
'q_levels':int(metric_params_list[2]),
'max_inner_iter':int(metric_params_list[3]),
'use_double_gradient':False if params.single_gradient else True}
similarity_metric = EMMetric(3, metric_parameters)
elif metric_name=='CC':
metric_parameters = {
'max_step_length':float(metric_params_list[0]),
'sigma_diff':float(metric_params_list[1]),
'radius':int(metric_params_list[2])}
similarity_metric = CCMetric(3, metric_parameters)
optimizer_parameters = {
'max_iter':[int(i) for i in params.iter.split(',')],
'inversion_iter':int(params.inversion_iter),
'inversion_tolerance':float(params.inversion_tolerance),
'report_status':True if params.report_status else False}
moving = nib.load(params.target)
moving_affine = moving.get_affine()
fixed = nib.load(params.reference)
fixed_affine = fixed.get_affine()
print 'Affine:', params.affine
if not params.affine:
transform = np.eye(4)
else:
transform = rcommon.readAntsAffine(params.affine)
init_affine = np.linalg.inv(moving_affine).dot(transform.dot(fixed_affine))
#print initAffine
moving = moving.get_data().squeeze().astype(np.float64)
fixed = fixed.get_data().squeeze().astype(np.float64)
moving = moving.copy(order='C')
fixed = fixed.copy(order='C')
moving = (moving-moving.min())/(moving.max()-moving.min())
fixed = (fixed-fixed.min())/(fixed.max()-fixed.min())
###################Run registration##################
update_rule = UpdateRule.Composition()
registration_optimizer = SymmetricRegistrationOptimizer(
fixed, moving, None, init_affine, similarity_metric, update_rule,
optimizer_parameters)
registration_optimizer.optimize()
displacement = registration_optimizer.get_forward()
inverse = registration_optimizer.get_backward()
del registration_optimizer
del similarity_metric
del update_rule
save_registration_results(init_affine, displacement, inverse, params)
def test_exec():
target='target/IBSR_01_ana_strip.nii.gz'
reference='reference/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz'
affine='IBSR_01_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeledAffine.txt'
paramiter='30x30x0'
inversion_iter='20'
inversion_tolerance='1e-3'
report_status=True
print('Registering %s to %s'%(target, reference))
sys.stdout.flush()
####Initialize parameter dictionaries####
metric_parameters = {
'max_step_length':0.25,
'sigma_diff':3.0,
'radius':4}
similarity_metric = CCMetric(3, metric_parameters)
optimizer_parameters = {
'max_iter':[int(i) for i in paramiter.split(',')],
'inversion_iter':int(inversion_iter),
'inversion_tolerance':float(inversion_tolerance),
'report_status':True if report_status else False}
moving = nib.load(target)
moving_affine = moving.get_affine()
fixed = nib.load(reference)
fixed_affine = fixed.get_affine()
print 'Affine:', affine
if not affine:
transform = np.eye(4)
else:
transform = rcommon.readAntsAffine(affine)
init_affine = np.linalg.inv(moving_affine).dot(transform.dot(fixed_affine))
#print initAffine
moving = moving.get_data().squeeze().astype(np.float64)
fixed = fixed.get_data().squeeze().astype(np.float64)
moving = moving.copy(order='C')
fixed = fixed.copy(order='C')
moving = (moving-moving.min())/(moving.max()-moving.min())
fixed = (fixed-fixed.min())/(fixed.max()-fixed.min())
###################Run registration##################
update_rule = UpdateRule.Composition()
registration_optimizer = SymmetricRegistrationOptimizer(
fixed, moving, None, init_affine, similarity_metric, update_rule,
optimizer_parameters)
registration_optimizer.optimize()
#displacement = registration_optimizer.get_forward()
#inverse = registration_optimizer.get_backward()
del registration_optimizer
del similarity_metric
del update_rule
def testEstimateMultimodalSyN3DMultiScale(fnameMoving, fnameFixed, fnameAffine,
warpDir, lambdaParam):
'''
testEstimateMultimodalDiffeomorphicField3DMultiScale('IBSR_01_ana_strip.nii.gz', 't1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'IBSR_01_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeledAffine.txt', 100)
'''
print 'Registering', fnameMoving, 'to', fnameFixed, 'with lambda=', lambdaParam
sys.stdout.flush()
moving = nib.load(fnameMoving)
fixed = nib.load(fnameFixed)
referenceShape = np.array(fixed.shape, dtype=np.int32)
M = moving.get_affine()
F = fixed.get_affine()
if not fnameAffine:
T = np.eye(4)
else:
T = rcommon.readAntsAffine(fnameAffine)
initAffine = np.linalg.inv(M).dot(T.dot(F))
print initAffine
moving = moving.get_data().squeeze().astype(np.float64)
fixed = fixed.get_data().squeeze().astype(np.float64)
moving = np.copy(moving, order='C')
fixed = np.copy(fixed, order='C')
moving = (moving - moving.min()) / (moving.max() - moving.min())
fixed = (fixed - fixed.min()) / (fixed.max() - fixed.min())
level = 2
maskMoving = moving > 0
maskFixed = fixed > 0
movingPyramid = [
img for img in rcommon.pyramid_gaussian_3D(moving, level, maskMoving)
]
fixedPyramid = [
img for img in rcommon.pyramid_gaussian_3D(fixed, level, maskFixed)
]
#maxOuterIter=[25,50,100,100, 100, 100]
maxOuterIter = [2, 2, 2, 2, 2, 2]
baseMoving = rcommon.getBaseFileName(fnameMoving)
baseFixed = rcommon.getBaseFileName(fnameFixed)
# if(os.path.exists('disp_'+baseMoving+'_'+baseFixed+'.npy')):
# displacement=np.load('disp_'+baseMoving+'_'+baseFixed+'.npy')
# else:
displacement, directInverse = estimateMultimodalSyN3DMultiScale(
movingPyramid, fixedPyramid, initAffine, lambdaParam, maxOuterIter, 0)
tf.prepend_affine_to_displacement_field(displacement, initAffine)
# np.save('disp_'+baseMoving+'_'+baseFixed+'.npy', displacement)
#####Warp all requested volumes
#---first the target using tri-linear interpolation---
moving = nib.load(fnameMoving).get_data().squeeze().astype(np.float64)
moving = np.copy(moving, order='C')
warped = np.array(tf.warp_volume(moving, displacement)).astype(np.int16)
imgWarped = nib.Nifti1Image(warped, F)
imgWarped.to_filename('warpedDiff_' + baseMoving + '_' + baseFixed +
'.nii.gz')
#---warp using affine only
moving = nib.load(fnameMoving).get_data().squeeze().astype(np.int32)
moving = np.copy(moving, order='C')
warped = np.array(
tf.warp_discrete_volumeNNAffine(moving, referenceShape,
initAffine)).astype(np.int16)
imgWarped = nib.Nifti1Image(
warped, F) #The affine transformation is the reference's one
imgWarped.to_filename('warpedAffine_' + baseMoving + '_' + baseFixed +
'.nii.gz')
#---now the rest of the targets using nearest neighbor
names = [os.path.join(warpDir, name) for name in os.listdir(warpDir)]
for name in names:
#---warp using the non-linear deformation
toWarp = nib.load(name).get_data().squeeze().astype(np.int32)
toWarp = np.copy(toWarp, order='C')
baseWarp = rcommon.getBaseFileName(name)
warped = np.array(tf.warp_discrete_volumeNN(
toWarp, displacement)).astype(np.int16)
imgWarped = nib.Nifti1Image(
warped, F) #The affine transformation is the reference's one
imgWarped.to_filename('warpedDiff_' + baseWarp + '_' + baseFixed +
'.nii.gz')
#---warp using affine inly
warped = np.array(
tf.warp_discrete_volumeNNAffine(toWarp, referenceShape,
initAffine)).astype(np.int16)
imgWarped = nib.Nifti1Image(
warped, F) #The affine transformation is the reference's one
imgWarped.to_filename('warpedAffine_' + baseWarp + '_' + baseFixed +
'.nii.gz')
#---finally, the deformed lattices (forward, inverse and resdidual)---
lambdaParam = 0.9
maxIter = 100
tolerance = 1e-4
print 'Computing inverse...'
inverse = np.array(
tf.invert_vector_field3D(displacement, lambdaParam, maxIter,
tolerance))
residual = np.array(tf.compose_vector_fields3D(displacement, inverse))
saveDeformedLattice3D(
displacement,
'latticeDispDiff_' + baseMoving + '_' + baseFixed + '.nii.gz')
saveDeformedLattice3D(
inverse, 'latticeInvDiff_' + baseMoving + '_' + baseFixed + '.nii.gz')
saveDeformedLattice3D(
residual, 'latticeResdiff_' + baseMoving + '_' + baseFixed + '.nii.gz')
residual = np.sqrt(np.sum(residual**2, 3))
print "Mean residual norm:", residual.mean(), " (", residual.std(
), "). Max residual norm:", residual.max()
def testEstimateMultimodalSyN3DMultiScale(fnameMoving, fnameFixed, fnameAffine, warpDir, lambdaParam):
'''
testEstimateMultimodalDiffeomorphicField3DMultiScale('IBSR_01_ana_strip.nii.gz', 't1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'IBSR_01_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeledAffine.txt', 100)
'''
print 'Registering', fnameMoving, 'to', fnameFixed,'with lambda=',lambdaParam
sys.stdout.flush()
moving = nib.load(fnameMoving)
fixed= nib.load(fnameFixed)
referenceShape=np.array(fixed.shape, dtype=np.int32)
M=moving.get_affine()
F=fixed.get_affine()
if not fnameAffine:
T=np.eye(4)
else:
T=rcommon.readAntsAffine(fnameAffine)
initAffine=np.linalg.inv(M).dot(T.dot(F))
print initAffine
moving=moving.get_data().squeeze().astype(np.float64)
fixed=fixed.get_data().squeeze().astype(np.float64)
moving=np.copy(moving, order='C')
fixed=np.copy(fixed, order='C')
moving=(moving-moving.min())/(moving.max()-moving.min())
fixed=(fixed-fixed.min())/(fixed.max()-fixed.min())
level=2
maskMoving=moving>0
maskFixed=fixed>0
movingPyramid=[img for img in rcommon.pyramid_gaussian_3D(moving, level, maskMoving)]
fixedPyramid=[img for img in rcommon.pyramid_gaussian_3D(fixed, level, maskFixed)]
#maxOuterIter=[25,50,100,100, 100, 100]
maxOuterIter=[2,2,2,2,2,2]
baseMoving=rcommon.getBaseFileName(fnameMoving)
baseFixed=rcommon.getBaseFileName(fnameFixed)
# if(os.path.exists('disp_'+baseMoving+'_'+baseFixed+'.npy')):
# displacement=np.load('disp_'+baseMoving+'_'+baseFixed+'.npy')
# else:
displacement, directInverse=estimateMultimodalSyN3DMultiScale(movingPyramid, fixedPyramid, initAffine, lambdaParam, maxOuterIter, 0)
tf.prepend_affine_to_displacement_field(displacement, initAffine)
# np.save('disp_'+baseMoving+'_'+baseFixed+'.npy', displacement)
#####Warp all requested volumes
#---first the target using tri-linear interpolation---
moving=nib.load(fnameMoving).get_data().squeeze().astype(np.float64)
moving=np.copy(moving, order='C')
warped=np.array(tf.warp_volume(moving, displacement)).astype(np.int16)
imgWarped=nib.Nifti1Image(warped, F)
imgWarped.to_filename('warpedDiff_'+baseMoving+'_'+baseFixed+'.nii.gz')
#---warp using affine only
moving=nib.load(fnameMoving).get_data().squeeze().astype(np.int32)
moving=np.copy(moving, order='C')
warped=np.array(tf.warp_discrete_volumeNNAffine(moving, referenceShape, initAffine)).astype(np.int16)
imgWarped=nib.Nifti1Image(warped, F)#The affine transformation is the reference's one
imgWarped.to_filename('warpedAffine_'+baseMoving+'_'+baseFixed+'.nii.gz')
#---now the rest of the targets using nearest neighbor
names=[os.path.join(warpDir,name) for name in os.listdir(warpDir)]
for name in names:
#---warp using the non-linear deformation
toWarp=nib.load(name).get_data().squeeze().astype(np.int32)
toWarp=np.copy(toWarp, order='C')
baseWarp=rcommon.getBaseFileName(name)
warped=np.array(tf.warp_discrete_volumeNN(toWarp, displacement)).astype(np.int16)
imgWarped=nib.Nifti1Image(warped, F)#The affine transformation is the reference's one
imgWarped.to_filename('warpedDiff_'+baseWarp+'_'+baseFixed+'.nii.gz')
#---warp using affine inly
warped=np.array(tf.warp_discrete_volumeNNAffine(toWarp, referenceShape, initAffine)).astype(np.int16)
imgWarped=nib.Nifti1Image(warped, F)#The affine transformation is the reference's one
imgWarped.to_filename('warpedAffine_'+baseWarp+'_'+baseFixed+'.nii.gz')
#---finally, the deformed lattices (forward, inverse and resdidual)---
lambdaParam=0.9
maxIter=100
tolerance=1e-4
print 'Computing inverse...'
inverse=np.array(tf.invert_vector_field3D(displacement, lambdaParam, maxIter, tolerance))
residual=np.array(tf.compose_vector_fields3D(displacement, inverse))
saveDeformedLattice3D(displacement, 'latticeDispDiff_'+baseMoving+'_'+baseFixed+'.nii.gz')
saveDeformedLattice3D(inverse, 'latticeInvDiff_'+baseMoving+'_'+baseFixed+'.nii.gz')
saveDeformedLattice3D(residual, 'latticeResdiff_'+baseMoving+'_'+baseFixed+'.nii.gz')
residual=np.sqrt(np.sum(residual**2,3))
print "Mean residual norm:", residual.mean()," (",residual.std(), "). Max residual norm:", residual.max()
def showRegistrationResultMidSlices(fnameMoving, fnameFixed, fnameAffine=None):
'''
showRegistrationResultMidSlices('IBSR_01_ana_strip.nii.gz', 't1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'IBSR_01_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeledAffine.txt')
showRegistrationResultMidSlices('warpedDiff_IBSR_01_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 't1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_01_ana_strip_IBSR_02_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_02/IBSR_02_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_01_segTRI_ana_IBSR_02_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_02/IBSR_02_segTRI_ana.nii.gz', None)
##Worst pair:
showRegistrationResultMidSlices('warpedDiff_IBSR_16_segTRI_ana_IBSR_12_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_12/IBSR_12_segTRI_ana.nii.gz', None)
showRegistrationResultMidSlices('/opt/registration/data/t1/IBSR18/IBSR_16/IBSR_16_segTRI_ana.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_12/IBSR_12_segTRI_ana.nii.gz', None)
showRegistrationResultMidSlices('/opt/registration/data/t1/IBSR18/IBSR_16/IBSR_16_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_12/IBSR_12_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedAffine_IBSR_16_segTRI_ana_IBSR_12_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_12/IBSR_12_segTRI_ana.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_16_ana_strip_IBSR_12_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_12/IBSR_12_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedAffine_IBSR_16_ana_strip_IBSR_12_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_12/IBSR_12_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_16/IBSR_16_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedAffine_IBSR_10_ana_strip_IBSR_16_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_16/IBSR_16_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedAffine_IBSR_16_ana_strip_IBSR_10_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_16_ana_strip_IBSR_10_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_01_ana_strip_IBSR_08_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_08/IBSR_08_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('/opt/registration/data/t1/IBSR18/IBSR_01/IBSR_01_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_08/IBSR_08_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_13_ana_strip_IBSR_10_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedAffine_IBSR_13_ana_strip_IBSR_10_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('/opt/registration/data/t1/IBSR18/IBSR_13/IBSR_13_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_01_ana_strip_IBSR_02_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedAffine_IBSR_16_seg_ana_IBSR_10_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_seg_ana.nii.gz', None)
showRegistrationResultMidSlices('/opt/registration/data/t1/IBSR18/IBSR_16/IBSR_16_seg_ana.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_seg_ana.nii.gz', None)
showRegistrationResultMidSlices('/opt/registration/data/t1/IBSR18/IBSR_01/IBSR_01_segTRI_fill_ana.nii.gz', 'warpedAffine_IBSR_10_segTRI_fill_ana_IBSR_01_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_07_ana_strip_IBSR_17_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_17/IBSR_17_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('/opt/registration/data/t1/IBSR18/IBSR_07/IBSR_07_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_17/IBSR_17_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_06_ana_strip_IBSR_17_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_17/IBSR_17_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_07_ana_strip_IBSR_12_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_12/IBSR_12_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_15_ana_strip_IBSR_10_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_01_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 't1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_01_segTRI_fill_ana_t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'data/phantom_1.0mm_normal_crisp.rawb.nii.gz', None)
showRegistrationResultMidSlices('data/t1/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'data/phantom_1.0mm_normal_crisp_peeled.nii.gz', None)
showRegistrationResultMidSlices('data/t2/t2_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'data/phantom_1.0mm_normal_crisp_peeled.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_16_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'data/t1/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', None)
showRegistrationResultMidSlices('warpedAffine_IBSR_16_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'data/t1/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', None)
showRegistrationResultMidSlices('test16.nii.gz', 'data/t1/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', None)
showRegistrationResultMidSlices('data/t1/t1_icbm_normal_1mm_pn0_rf0.rawb_peeled.nii.gz', 'data/t1/t1_icbm_normal_1mm_pn0_rf0.rawb_peeled.nii.gz', None)
showRegistrationResultMidSlices('warpedAffine_IBSR_15_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'data/t1/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_15_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'data/t1/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', None)
showRegistrationResultMidSlices('warpedAffine_IBSR_01_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'data/t1/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_01_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'data/t1/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', None)
'''
if(fnameAffine==None):
T=np.eye(4)
else:
T=rcommon.readAntsAffine(fnameAffine)
print 'T:',T
fixed=nib.load(fnameFixed)
F=fixed.get_affine()
print 'F:',F
fixed=fixed.get_data().squeeze().astype(np.float64)
moving=nib.load(fnameMoving)
M=moving.get_affine()
print 'M:',M
moving=moving.get_data().squeeze().astype(np.float64)
initAffine=np.linalg.inv(M).dot(T.dot(F))
fixed=np.copy(fixed, order='C')
moving=np.copy(moving, order='C')
warped=np.array(tf.warp_volume_affine(moving, np.array(fixed.shape).astype(np.int32), initAffine))
sh=warped.shape
rcommon.overlayImages(warped[sh[0]//2,:,:], fixed[sh[0]//2,:,:])
rcommon.overlayImages(warped[:,sh[1]//2,:], fixed[:,sh[1]//2,:])
rcommon.overlayImages(warped[:,:,sh[2]//2], fixed[:,:,sh[2]//2])
